Making Predictions with Sequences Using Recurrent Neural Networks
In the previous chapter, we focused on convolutional neural networks (CNNs) and used them to deal with image-related tasks. In this chapter, we will explore recurrent neural networks (RNNs), which are suitable for sequential data and time-dependent data, such as daily temperature, DNA sequences, and customers' shopping transactions over time. You will learn how the recurrent architecture works and see variants of the model. We will then work on their applications, including sentiment analysis and text generation. Finally, as a bonus section, we will cover a recent state-of-the-art sequential learning model: the Transformer.
We will cover the following topics in this chapter:
- Sequential learning by RNNs
- Mechanisms and training of RNNs
- Different types of RNNs
- Long Short-Term Memory RNNs
- RNNs for sentiment analysis
- RNNs for text generation
- Self-attention and the Transformer...
